5b48f9a9c097d26d395873044ceaa1a0b886682a,solutionbox/code_free_ml/mltoolbox/code_free_ml/analyze.py,,run_cloud_analysis,#Any#Any#Any#Any#Any#,161

Before Change

      numerical_vocab_stats[col_name] = {"min": df.iloc[0]["min_value"],
                                         "max": df.iloc[0]["max_value"],
                                         "mean": df.iloc[0]["avg_value"]}
    elif transform == constant.IMAGE_TRANSFORM:
      pass
    elif transform == constant.KEY_TRANSFORM:
      pass
    else:
      raise ValueError("Unknown transform %s" % transform)

  // get num examples
  sql = "SELECT count(*) as num_examples from {table}".format(table=table_name)
  df = _execute_sql(sql, table)

After Change

  // Make a copy of inverted_features and update the target transform to be
  // identity or one hot depending on the schema.
  inverted_features_target = copy.deepcopy(inverted_features)
  for name, transform_set in six.iteritems(inverted_features_target):
    if transform_set == set([constant.TARGET_TRANSFORM]):
      target_schema = next(col["type"].lower() for col in schema if col["name"] == name)
      if target_schema in constant.NUMERIC_SCHEMA:
        inverted_features_target[name] = {constant.IDENTITY_TRANSFORM}
      else:
        inverted_features_target[name] = {constant.ONE_HOT_TRANSFORM}

  numerical_vocab_stats = {}
  for col_name, transform_set in six.iteritems(inverted_features_target):
    // All transforms in transform_set require the same analysis. So look
    // at the first transform.

Instances